| Millimeter wave communication technology is a typical dual-use technology for military and civilian applications.In the military sector it can be used for interplanetary communication or relay,secure communication in the millimetre wave band,etc.In the civilian sector it can be used for broadband multimedia mobile communication systems,measurement radars,millimetre wave link systems for satellites and many other applications,and will further expand its market.By utilising a large scale array of antennas,millimetre wave MIMO(Multiple-Input Multiple-Output)UAV communications can produce high radial gain at certain orientations and is suitable for UAVs with space and energy constraints.The short wavelength of millimetre wave signals allows a large antenna array to be equipped even in very small areas.Millimetre wave communications with flexible beamforming can therefore support UAV communications.However,in UAV communication systems,Doppler shift of the UAV is unavoidable in the case of UAVs moving at high speeds.UAV jitter under the influence of wind can lead to beam misalignment,which complicates channel estimation and beamforming between the UAV and ground users.Channel fading is particularly evident in millimetre wave massive MIMO UAV communication systems.Radar systems have advantages such as easy processing,which facilitates channel estimation in millimetre-wave large-scale MIMO communication systems.Tensor is able to decompose high-dimensional signals into low-dimensional signals,minimising the complexity of the computation.Reconfigurable Intelligent Surface(RIS,Reconfigurable Intelligent Surface)assisted millimetre wave massive MIMO communication has been envisaged as a prominent technology for future wireless networks,as it can provide rich spectrum resources and a good propagation environment,which can better enhance the performance of communication systems.In this thesis,the following research will be conducted.Firstly,a TT(Tensor Train)tensor-based frequency diversity array(FDA,Frequency Diverse Array)MIMO radar-assisted millimetre-wave massive MIMO UAV communication system is investigated for the large number of complex calculations due to the Doppler shift and high-dimensional channels caused by the high mobility of UAVs in the millimetre-wave massive MIMO UAV communication system.channel estimation method for MIMO UAV communication system.The channel model of the FDA-MIMO radar-assisted millimeter-wave massive MIMO UAS is first established,and the interference problem between the radar system and the UAS communication system is handled through the radar control centre.The gridless method based on the FDA-MIMO radar is used to accurately and efficiently estimate the arrival and departure angles of the signal,which solves the angle estimation problem in the radar system due to the grid-based angle estimation method.The power leakage problem due to the gridded angle estimation method is solved.Then,a blind equalisation algorithm is used to transform the estimated angle information from the radar system into coordinates so that it can be received and utilised by the millimetrewave large-scale MIMO UAV communication system.Finally,the multi-dimensional signal of the radar-assisted millimetre-wave MIMO UAS received at the ground singleuser side is represented as a low-rank tensor signal by TT tensor decomposition,and the decomposition factor matrix is used to accurately estimate the fast fading channel power fading factor,path gain and Doppler shift.The convergence of the decomposition factor of the received signal at the terrestrial single-user end is improved by the power power power(PM,Power Method).Simulation results illustrate that the method proposed in this chapter has higher channel estimation accuracy.Secondly,a RIS-assisted millimeter-wave MIMO UAV hybrid beamforming method is proposed to improve the spectral efficiency of the system in urban environments due to the jitter that causes beam misalignment and thus indoor blindness.The problem of maximising the spectral efficiency of the system is decomposed into two sub-problems:optimising the RIS phase shift matrix and solving the hybrid beamforming matrix,and performing a singular value decomposition(SVD,Singular Value Decomposition).After decomposition,the RIS phase shift matrix is optimised.The hybrid beamforming matrix is then solved by decoupling the transmit and receive sides using an improved hybrid beamforming method based on the low-complexity Phase Extraction Alternating Minimization(PE-AltMin)algorithm.Simulation experiments demonstrate that the method proposed in this chapter has higher spectrum utilisation than other methods.Finally,the methods of the first two chapters are experimentally validated by constructing experimental scenarios in which the performance of the two methods is evaluated under different experimental scenarios.The experimental results show that the methods in the first two chapters can largely improve the performance of the overall millimetre wave massive MIMO UAV communication system. |
